## Maria Lomeli

## About me

I am a research engineer at Fundamental AI Research, Meta. Previously, I was a senior research scientist at Babylon Health, UK. Before that, I was a research associate, working with Zoubin Ghahramani at the Machine Learning group, CBL, University of Cambridge and member of Trinity Hall college.
I studied my PhD at the Gatsby Unit, UCL, my supervisor was Yee Whye Teh.
## Journal publications

Petroni, F., Broscheit, S., Piktus, A., Lewis, P., Izacard, G., Hosseini, L., Dwivedi-Yu, J., Lomeli, M., Schick, T., Mazaré, P.E., Joulin, A., Grave, E., Riedel, S., **''Improving wikipedia verifiability with AI''** Nature Machine Intelligence, 2023, Vol 5, pp 1142–1148, NMI.
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E., **''Atlas: few-shot learning with retrieval-augmented language models''** Journal of Machine Learning Research, 2023, Vol 24, pp 1-43, JMLR.
Mialon, G., Dessi, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rosiere, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., LeCun, Y., Scialom, T., **''Augmented language models: a survey''**, Transactions of Machine Learning Research, 2023, Vol 6, TLMR .
Valera, I., Pradier, M., Lomeli, M. and Ghahramani, Z., ** ''General Latent Feature Model for Heterogeneous Datasets''**, Journal of Machine Learning Research, 2020, Vol 21 JMLR.
Lomeli, M., Rowland, M., Gretton, A. and Ghahramani, Z.,** ''Antithetic and Monte Carlo kernel estimators for partial rankings''**, Statistics and Computing, 2019, Vol 29,1127–1147, StCo.
Lomeli, M., Favaro, S., Teh, Y. W.,**'' A marginal sampler for -Stable Poisson-Kingman mixture models''**, Journal of Computational and Graphical Statistics, 2017, Vol 26,pp 44-53 JCGS.
Favaro, S., Lomeli, M., Nipoti, B., Teh, Y.W., ** ''Stick-breaking representations of -stable Poisson-Kingman models'' **, Electronic Journal of Statistics, 2014, Vol. 8, pp 1063-1085 EJS.
Favaro, S., Lomeli, M., Teh, Y.W.,**''On a class of -stable Poisson-Kingman models and an effective marginalized sampler''**, Statistics and Computing, 2014, Vol 25, pp 67-78
StCo.
## Proceedings

Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T., 2023, **''Toolformer: language models can teach themselves to use tools''** Neurips *(accepted as an oral presentation)*.
Lomeli, M., Favaro, S.,Teh, Y.W., 2015, **''A hybrid sampler for Poisson-Kingman mixture models''**, Neural information Processing Systems NeurIPS
Sejdinovic, D., Strathmann, H., Lomeli Garcia, M., Andrieu, C., Gretton, A., 2014,**''Kernel Adaptive Metropolis-Hastings''**, International Conference in Machine Learning ICML
Harman, M., Ahlgren, J., Berezin, M., Dulskyte, E., Dvortsova, I., George, J., Gucevska, N., Meijer, E., Spahr-Summers, J., Bojarczuk, K., Sapora, S. and Lomeli, M., 2021, **''Testing Web Enabled Simulation at Scale Using Metamorphic Testing''**, International Conference on Software Engineering ICSE
## Workshops

Gautam, D., Lomeli, M., Gourgoulias, K., Thompson, D., Johri, S., 2019, **''Masking schemes for universal marginalisers''**, Advances in Approximate Bayesian Inference symposium
Bloem-Reddy, B., Mathieu, E., Foster, A., Rainforth, T., Ge, H., Lomeli, M., Ghahramani, Z., Teh, Y.W., 2017, ** ''Sampling and inference for discrete random probability measures in probabilistic programs''**, Advances in Approximate Bayesian Inference workshop, NeurIPS
## Thesis

** General Bayesian inference schemes in infinite mixture models**

PhD thesis, University College London

*UCL repository: Doctoral dissertation link *

## Preprints

Shi, W., Min, S., Lomeli, M., Chou, Z., Li, M., Lin, V., Smith, N. A., Zettlemoyer, L., Yih, S., Lewis, M. 2023, **In-Context Pretraining: Language Modeling Beyond Document Boundaries ** arXiv.
Lin, V. X., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., Zettlemoyer, L., Yih, S., 2023. **''RA-DIT: Retrieval-Augmented Dual Instruction Tuning'' ** arXiv.
Dwivedi-Yu, J., Schick, T., Jiang, Z., Lomeli, M., Lewis, P., Izacard, G., Grave, E., Riedel, S., Petroni, F., 2022, **''EditEval: an instruction-based benchmark for text omprovements''** arXiv.
## Talks

July, 2023. Talk at the Microsoft Research Montreal and Mila seminar, Montreal, Canada (virtual)
June, 2023. Talk at the Gatsby unit anniversary symposium, UCL, London
June, 2023. Talk at the virtual seminar series Sinc instutite, Santa Fe, Argentina (virtual)
March, 2023. Talk at the NLP parallel session, Latin American Meeting in Artificial Intelligence, Khipu, Montevideo, Uruguay
October, 2021. Joint keynote talk with Mark Harman at the ESEM 2021 conference (virtual)
September, 2019. Talk at the ''Recent developments on kernel methods'', UCL, London
July, 2019. Talk at the Gatsby unit anniversary symposium, UCL, London
June, 2019. Talk at ''Congreso Bayesiano de América Latina'', Lima, Peru
April, 2019. Talk at ''Achieving impact in healthcare: from mathematics to clinical support systems and devices'' workshop, Newton Institute, Cambridge, UK
March 20, 2019. Talk at the Statistics seminar series, Queen Mary University, London
June 11, 2018. Talk at Parallelising Monte Carlo Algorithms workshop, School of Mathematics, University of Bristol, Bristol
March 15, 2018. Talk at the CamAIML event, Microsoft research Cambridge, Cambridge, UK
February 16, 2018. Talk at the University of Glasgow, Statistics seminar, Glasgow, UK
Febryary 2, 2018. Talk at UCL, CSML Lunchtime seminar, London, UK
February 1, 2018. Talk at Amazon Cambridge research series seminar, Cambridge, UK
August 30, 2017. Talk at the 2017 SMC workshop, Uppsala, Sweden
June 7, 2017. Talk at the ''Congreso Bayesiano de América Latina'', Guanajuato, Mexico
June 14, 2016. Talk at the ''Bayes Legacy'' sesssion, 13th ISBA Wold meeting in Sardinia, Italy
June 2, 2016. Talk at the Machine Learning group, CBL, University of Cambridge
May 5, 2016. Talk at Machine Learning reading group, CBL, University of Cambridge
July 16, 2015. Talk at CBL, University of Cambridge
June 22, 2015. Talk at the 10th
Bayesian Nonparametrics conference
June 15, 2015. Talk at the 9th
Bayesian Inference for Stochastic Processes conference
January 26, 2014. Talk
at the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas,
Universidad Nacional Autónoma de México
October 24, 2014. Talk
at the Computational Statistics seminar, University of Oxford
September 24, 2014. Talk
at CBL, University of Cambridge
March 3, 2014. Talk at the workshop
Advances in Scalable Bayesian Computation, available
online here

## Teaching

Teaching assistant, Part II Statistical modelling course, Statslab, University of Cambridge (Lent, 2018)
Coding lab demonstrator, APTS, Statistical computing module for Statistics PhD students, University of Cambridge (December, 2017)
Coding lab demonstrator, MLSALT1 graduate course, University of Cambridge (Michaelmas, 2017)
Coding lab demonstrator, 3F8 undergraduate course, University of Cambridge (Lent, 2017)
Teaching assistant, Statistical Data Mining and Machine Learning MSc in Applied Statistics course, University of Oxford (Hilary term 2014 and 2015)

Coding lab demonstrator, Kernel methods module, Introduction to machine learning graduate course, University College London (2013)
Teaching assistant, Probabilistic and Unsupervised Learning graduate course, University College London (Autumn, 2012)

Lecturer, Stochastic Processes, undergraduate course, Instituto Tecnológico Autónomo de México (Summer, 2011)

## Reviewing

2023 Action Editor, ACL RR
2019, 2021, 2022 Uncertainty in Artificial Intelligence conference
2018, 2020 Bayesian Analysis
2018 Journal of Machine Learning Research
2017 Biometrika
2017 Scandinavian Journal of Statistics
2016 Computational Statistics and Data Analysis
2016 Statistics and Computing
2016, 2017, 2019 International Conference in Machine Learning
2013, 2014, 2015, 2017, 2018, 2019, 2023 Neural Information Processing Systems
2014, 2015, 2020, 2021, 2022
AISTATS
## Miscellaneous

I was one of the organisers of our CSML Lunch Talk Series.